Artificial intelligence-assisted determination of available sites for palatal orthodontic mini implants based on palatal thickness through CBCT.

OBJECTIVES: To develop an artificial intelligence (AI) system for automatic palate segmentation through CBCT, and to determine the personalized available sites for palatal mini implants by measuring palatal bone and soft tissue thickness according to the AI-predicted results. MATERIALS AND METHODS: Eight thousand four hundred target slices (from 70 CBCT scans) from orthodontic patients were collected, labelled by well-trained orthodontists and randomly divided into two groups: a training set and a test set. After the deep learning process, we evaluated the performance of our deep learning model with the mean Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), sensitivity (SEN), positive predictive value (PPV) and mean thickness percentage error (MTPE). The pixel traversal method was proposed to measure the thickness of palatal bone and soft tissue, and to predict available sites for palatal orthodontic mini implants. Then, an example of available sites for palatal mini implants from the test set was mapped. RESULTS: The average DSC, ASSD, SEN, PPV and MTPE for the segmented palatal bone tissue were 0.831%, 1.122%, 0.876%, 0.815% and 6.70%, while that for the palatal soft tissue were 0.741%, 1.091%, 0.861%, 0.695% and 12.2%, respectively. Besides, an example of available sites for palatal mini implants was mapped according to predefined criteria. CONCLUSIONS: Our AI system showed high accuracy for palatal segmentation and thickness measurement, which is helpful for the determination of available sites and the design of a surgical guide for palatal orthodontic mini implants.

NGF-Induced Upregulation of CGRP in Orofacial Pain Induced by Tooth Movement Is Dependent on Atp6v0a1 and Vesicle Release.

The nerve growth factor (NGF) and calcitonin gene-related peptide (CGRP) play a crucial role in the regulation of orofacial pain. It has been demonstrated that CGRP increases orofacial pain induced by NGF. V-type proton ATPase subunit an isoform 1 (Atp6v0a1) is involved in the exocytosis pathway, especially in vesicular transport in neurons. The objective was to examine the role of Atp6v0a1 in NGF-induced upregulation of CGRP in orofacial pain induced by experimental tooth movement. Orofacial pain was elicited by ligating closed-coil springs between incisors and molars in Sprague-Dawley rats. Gene and protein expression levels were determined through real-time polymerase chain reaction, immunostaining, and fluorescence in situ hybridization. Lentivirus vectors carrying Atp6v0a1 shRNA were used to knockdown the expression of Atp6v0a1 in TG and SH-SY5Y neurons. The release of vesicles in SH-SY5Y neurons was observed by using fluorescence dye FM1-43, and the release of CGRP was detected by Enzyme-Linked Immunosorbent Assy. Orofacial pain was evaluated through the rat grimace scale. Our results revealed that intraganglionic administration of NGF and Atp6v0a1 shRNA upregulated and downregulated CGRP in trigeminal ganglia (TG) and trigeminal subnucleus caudalis (Vc), respectively, and the orofacial pain was also exacerbated and alleviated, respectively, following administration of NGF and Atp6v0a1 shRNA. Besides, intraganglionic administration of NGF simultaneously caused the downregulation of Atp6v0a1 in TG. Moreover, the release of vesicles and CGRP in SH-SY5Y neurons was interfered by NGF and Atp6v0a1 shRNA. In conclusion, in the orofacial pain induced by experimental tooth movement, NGF induced the upregulation of CGRP in TG and Vc, and this process is dependent on Atp6v0a1 and vesicle release, suggesting that they are involved in the transmission of nociceptive information in orofacial pain.